It is a problem to determine whether a subject is impaired, regardless of whether this is due to a medical condition, or the use of alcohol or drugs, or whether the subject is suffering from sleep deprivation, or any other condition which interferes with the subject's ability to perform their assigned tasks. This is particularly a problem in the workplace where an employee who is presently functioning at a level of alertness that is less than their norm can place themselves and others in danger of injury, especially when the impaired employee is operating machinery. In addition, an impaired employee functions at a reduced level of efficiency, and the incidence of errors in the impaired employee's work product dramatically increases.
There is presently no automated system for quickly and accurately determining the impairment of large numbers of employees, so employers resort to random and expensive drug testing to detect the presence of impaired employees in the workplace. For example, urine-based drug testing does not test for impairment, only prior use, is limited to a few illegal substances, and does not deal with alcohol, medical conditions, or fatigue. Therefore, while this approach fails to provide any degree of effective screening of the entire workforce on a daily basis, it is expected that it does have some deterrent effect which decreases the incidence of impaired employees.
It is known that the determination of a subject's impairment can be accomplished by the measurement of the subject's eye gaze tracking and the monitoring of the subject's eye behavior, which measurements are compared to a normal response or a normal response for the subject, to determine a subject's alertness and attention. For example, by tracking and measuring the pupil responses of the subject, it is possible to determine the subject's impairment, since abnormal pupil dilation and pupil response to a stimulus are involuntary responses. These responses, in association with eye movement in tracking a visual stimulus, can definitively determine the impairment in a subject.
With all of these metrics available as impairment determination tools, the accurate measurement and tracking of the subject's pupil centroid location and determination of the area of the pupil are critical to the precise determination of impairment. It is also important to detect eye blinking to ensure that pupil measurements are not computed at those time instances when a blink occurs, since the pupil is either partially visible or not visible at all when a blink occurs.
Presently, all of these measurements are performed manually, with the accuracy and reliability of these measurements being subjective and possibly tainted by the ambient conditions under which the test is administered. Ambient conditions include the inability to maintain the subject's gaze in a fixed direction, erratic lighting conditions, and the subjective nature of the test administrator's administration of the test.
There have been some previous computer-based attempts to automate this process and quickly determine the exact area of a subject's pupil. These proposed processes have relied on either finding the darkest area on the image of the eye, and then thresholding within a range of this value to extract the pupil, or by attempting to fit an ellipse of predetermined diameter to the features present in the input image. Both approaches fail at producing an exact measurement of the pupil's location and area when light specularities or long eyelashes occlude the pupil, and when other dark regions are detected in addition to or instead of the pupil due to the presence of heavy eye makeup or bushy eyebrows. These complex conditions cause the subject's pupil to have a non-circular shape, which would be deformed by the ellipse fitting eye-response tracking process normally used to detect pupils and irises in human eyes, and other approaches based on the detection of the darkest point in the image would fail in the presence of eye makeup or thick eyebrows. These methods also have no means to compensate for specularities that can occur due to the lighting conditions.
Therefore, presently there is no viable automated impairment detection system that can be used to screen large numbers of subjects to quickly and accurately determine impairment of a subject.